10 research outputs found
Out-of-Distribution Generalization in Algorithmic Reasoning Through Curriculum Learning
Out-of-distribution generalization (OODG) is a longstanding challenge for
neural networks, and is quite apparent in tasks with well-defined variables and
rules, where explicit use of the rules can solve problems independently of the
particular values of the variables. Large transformer-based language models
have pushed the boundaries on how well neural networks can generalize to novel
inputs, but their complexity obfuscates they achieve such robustness. As a step
toward understanding how transformer-based systems generalize, we explore the
question of OODG in smaller scale transformers. Using a reasoning task based on
the puzzle Sudoku, we show that OODG can occur on complex problems if the
training set includes examples sampled from the whole distribution of simpler
component tasks
Learning To Rank Diversely At Airbnb
Airbnb is a two-sided marketplace, bringing together hosts who own listings
for rent, with prospective guests from around the globe. Applying neural
network-based learning to rank techniques has led to significant improvements
in matching guests with hosts. These improvements in ranking were driven by a
core strategy: order the listings by their estimated booking probabilities,
then iterate on techniques to make these booking probability estimates more and
more accurate. Embedded implicitly in this strategy was an assumption that the
booking probability of a listing could be determined independently of other
listings in search results. In this paper we discuss how this assumption,
pervasive throughout the commonly-used learning to rank frameworks, is false.
We provide a theoretical foundation correcting this assumption, followed by
efficient neural network architectures based on the theory. Explicitly
accounting for possible similarities between listings, and reducing them to
diversify the search results generated strong positive impact. We discuss these
metric wins as part of the online A/B tests of the theory. Our method provides
a practical way to diversify search results for large-scale production ranking
systems.Comment: Search ranking, Diversity, e-commerc
Applying Deep Learning To Airbnb Search
The application to search ranking is one of the biggest machine learning
success stories at Airbnb. Much of the initial gains were driven by a gradient
boosted decision tree model. The gains, however, plateaued over time. This
paper discusses the work done in applying neural networks in an attempt to
break out of that plateau. We present our perspective not with the intention of
pushing the frontier of new modeling techniques. Instead, ours is a story of
the elements we found useful in applying neural networks to a real life
product. Deep learning was steep learning for us. To other teams embarking on
similar journeys, we hope an account of our struggles and triumphs will provide
some useful pointers. Bon voyage!Comment: 8 page
Optimizing Airbnb Search Journey with Multi-task Learning
At Airbnb, an online marketplace for stays and experiences, guests often
spend weeks exploring and comparing multiple items before making a final
reservation request. Each reservation request may then potentially be rejected
or cancelled by the host prior to check-in. The long and exploratory nature of
the search journey, as well as the need to balance both guest and host
preferences, present unique challenges for Airbnb search ranking. In this
paper, we present Journey Ranker, a new multi-task deep learning model
architecture that addresses these challenges. Journey Ranker leverages
intermediate guest actions as milestones, both positive and negative, to better
progress the guest towards a successful booking. It also uses contextual
information such as guest state and search query to balance guest and host
preferences. Its modular and extensible design, consisting of four modules with
clear separation of concerns, allows for easy application to use cases beyond
the Airbnb search ranking context. We conducted offline and online testing of
the Journey Ranker and successfully deployed it in production to four different
Airbnb products with significant business metrics improvements.Comment: Search Ranking, Recommender Systems, User Search Journey, Multi-task
learning, Two-sided marketplac